Financing drug development via adaptive platform trials.

We propose a new approach to funding disease-specific drug development via a variation of the adaptive platform trial. This trial is designed to test a portfolio of drug candidates in parallel, with the cost of the trial partially covered by investors who receive payments from a royalty fund of the candidates in exchange for investment. Under realistic assumptions for cost, revenue, probability of success, drug sales, and royalty rates, investors may expect a return of 28%, but with a 22% probability of total loss.

Predicting clinical trial duration via statistical and machine learning models.

We apply survival analysis as well as machine learning models to predict the duration of clinical trials using the largest dataset so far constructed in this domain. Neural network-based DeepSurv yields the most accurate predictions and we identify key factors that are most predictive of trial duration. This methodology may help clinical researchers optimize trial designs for expedited testing, and can also reduce the financial risk of drug development, which in turn will lower the cost of funding and increase the amount of capital allocated to this sector.

How does news affect biopharma stock prices?: An event study.

We investigate the impact of information on biopharmaceutical stock prices via an event study encompassing 503,107 news releases from 1,012 companies. We distinguish between pharmaceutical and biotechnology companies, and apply three asset pricing models to estimate their abnormal returns. Acquisition-related news yields the highest positive return, while drug-development setbacks trigger significant negative returns.

Incorporating patient preferences and burden-of-disease in evaluating ALS drug candidate AMX0035: a Bayesian decision analysis perspective.

Objective: Provide US FDA and amyotrophic lateral sclerosis (ALS) society with a systematic, transparent, and quantitative framework to evaluate the efficacy of the ALS therapeutic candidate AMX0035 in its phase 2 trial, which showed statistically significant effects (-value 3%) in slowing the rate of ALS progression on a relatively small sample size of 137 patients.

Methods: We apply Bayesian decision analysis (BDA) to determine the optimal type I error rate (-value) under which the clinical evidence of AMX0035 supports FDA approval. Using rigorous estimates of ALS disease burden, our BDA framework strikes the optimal balance between FDA's need to limit adverse effects (type I error) and patients' need for expedited access to a potentially effective therapy (type II error).